Would CUDA be a better path than Intel Arc A550M 8GB for DeepSeek R1 Distill Llama 8B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

Can DeepSeek R1 Distill Llama 8B run on Intel Arc A550M 8GB?

Q: What should I upgrade first if DeepSeek R1 Distill Llama 8B feels slow on Intel Arc A550M 8GB?

Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

YES — Tight Fit

C50Usable

Estimated from fit model

DeepSeek R1 Distill Llama 8B needs ~7.5 GB VRAM. Intel Arc A550M 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.5 GB, 22.5 tok/s, Tight fit

7.5 GB required8.0 GB available

94% VRAM used

Fit status

Tight fit

Decode

22.5 tok/s

TTFT

8608 ms

Safe context

24K

Memory

7.5 GB / 8.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill Llama 8B on Intel Arc A550M 8GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
  - Speed = 60 mph (miles per hour)
  - Time  = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
  Distance = 60 mph x 2.5 h
           = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
  - First half: 1.25 hours at 60 mph = 75 miles
  - Stop: 20 minutes (no distance)
  - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 22.5 tok/s decode · 8.6s TTFT (warm) · 56 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	22.5 tok/s	4695 ms	24K
Coding	C	Tight fit	22.5 tok/s	8608 ms	24K
Agentic Coding	D	Runs with offload (needs ~0.3 GB host RAM)	15.0 tok/s	18755 ms	24K
Reasoning	C	Tight fit	22.5 tok/s	10173 ms	24K
RAG	D	Runs with offload (needs ~0.3 GB host RAM)	15.0 tok/s	23444 ms

Quantization options

How DeepSeek R1 Distill Llama 8B (8B params) fits at each quantization level on Intel Arc A550M 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	C54
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 Distill Llama 8B on your machine.

Run

lms load hf-unsloth--deepseek-r1-distill-llama-8b-gguf && lms server start

Upgrade options

Hardware that runs DeepSeek R1 Distill Llama 8B well

Intel Arc B570 10GBBudget pick

10 GB VRAM (+2)380 GB/s (+156)

Raises estimated decode speed by about 87%.42 tok/s decode

Raises estimated decode speed by about 87%.

Adds memory headroom for longer context windows and future model growth.

~$219 MSRP

Intel Arc B580 12GBBest value

12 GB VRAM (+4)456 GB/s (+232)

Raises estimated decode speed by about 100%.44.9 tok/s decode

Raises estimated decode speed by about 100%.

Adds memory headroom for longer context windows and future model growth.

~$249 MSRP

Intel Arc A770 16GBIntel upgrade

16 GB VRAM (+8)560 GB/s (+336)

Raises estimated decode speed by about 129%.51.6 tok/s decode

Raises estimated decode speed by about 129%.

Adds memory headroom for longer context windows and future model growth.

~$349 MSRP

Frequently asked questions

Can Intel Arc A550M 8GB run DeepSeek R1 Distill Llama 8B?

Yes, Intel Arc A550M 8GB can run DeepSeek R1 Distill Llama 8B with a C grade (Tight fit). Expected decode speed: 22.5 tok/s.

How much VRAM does DeepSeek R1 Distill Llama 8B need?

DeepSeek R1 Distill Llama 8B (8B parameters) requires approximately 7.5 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill Llama 8B?

The recommended quantization for DeepSeek R1 Distill Llama 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill Llama 8B run at on Intel Arc A550M 8GB?

On Intel Arc A550M 8GB, DeepSeek R1 Distill Llama 8B achieves approximately 22.5 tokens per second decode speed with a time-to-first-token of 8608ms using Q4_K_M quantization.

Can Intel Arc A550M 8GB run DeepSeek R1 Distill Llama 8B for coding?

For coding workloads, DeepSeek R1 Distill Llama 8B on Intel Arc A550M 8GB receives a C grade with 22.5 tok/s and 24K context.

What context window can DeepSeek R1 Distill Llama 8B use on Intel Arc A550M 8GB?

On Intel Arc A550M 8GB, DeepSeek R1 Distill Llama 8B can safely use up to 24K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc A550M 8GB See all hardware for DeepSeek R1 Distill Llama 8B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-unsloth--deepseek-r1-distill-llama-8b-gguf-on-arc-a550m-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: